THREE ARGUMENTS AGAINST FOUNDATIONALISM: ARBITRARINESS, EPISTEMIC REGRESS, AND EXISTENTIAL SUPPORT forthcoming in Canadian Journal of Philosophy
Bibliographic record
Abstract
Abstract. Foundationalism is false; after all, foundational beliefs are arbitrary, they do not solve the epistemic regress problem, and they cannot exist without other (justified) beliefs. Or so some people say. In this essay, we assess some arguments based on such claims, arguments suggested in recent work by Peter Klein and Ernest Sosa. A particular belief of a person is basic just in case it is epistemically justified and it owes its justification to something other than her other beliefs or the interrelations of their contents; a person’s belief is nonbasic just in case it is epistemically justified but not basic. Traditional Foundationalism says that, first, if a human being has a nonbasic belief, then, at bottom, it owes its justification to at least one basic belief, and second, there are basic beliefs. Call the second thesis Minimal Foundationalism. In this essay, we assess three arguments against Minimal Foundationalism which we find in recent work of Peter Klein and Ernest Sosa. 1 1. Foundationalism and Arbitrariness Peter Klein puts his case against Foundationalism succinctly as follows: [F]oundationalism is unacceptable because it advocates accepting an arbitrary reason at
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".